唐超权, 王明辉, 李斌, 马书根. 融合机械元的蛇形机器人循环抑制中枢模式发生器控制方法[J]. 机器人, 2013, 35(1): 123-128. DOI: 10.3724/SP.J.1218.2013.00123
引用本文: 唐超权, 王明辉, 李斌, 马书根. 融合机械元的蛇形机器人循环抑制中枢模式发生器控制方法[J]. 机器人, 2013, 35(1): 123-128. DOI: 10.3724/SP.J.1218.2013.00123
TANG Chaoquan, WANG Minghui, LI Bin, MA Shugen. A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots[J]. ROBOT, 2013, 35(1): 123-128. DOI: 10.3724/SP.J.1218.2013.00123
Citation: TANG Chaoquan, WANG Minghui, LI Bin, MA Shugen. A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots[J]. ROBOT, 2013, 35(1): 123-128. DOI: 10.3724/SP.J.1218.2013.00123

融合机械元的蛇形机器人循环抑制中枢模式发生器控制方法

A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots

  • 摘要: 针对蛇形机器人中枢模式发生器(CPG)控制中控制信号以及传感信息缺少选择依据的问题,提出了一种融合了机械元的循环抑制CPG控制方法.首先,将蛇形机器人本体动力学方程改造为机械元引入循环抑制CPG模型.其次,提出了改进的Matsuoka神经元,从而使得神经元与机械元具有一致的表达形式.再次,分析了融入机械元的循环抑制CPG模型中的参数关系,并给出了控制信号和传感信息与CPG状态量关系的表达式.最后,利用仿真对所提出的方法进行了验证,并对产生结果进行了分析.该方法中蛇形机器人的控制信号与传感信息都具有明确的定义,且由于用机械元的物理结构代替了神经元的计算,降低了CPG的计算量.

     

    Abstract: To solve the problem that there is no basis for choosing control signals and sensor information for central pattern generator (CPG) control of snake robots, a cyclic inhibitory CPG control method with mechanical oscillators is proposed. Firstly, the mechanical oscillators reformed from snake robot dynamical equations are introduced into the cyclic inhibitory CPG model. Secondly, an improved Matsuoka neuron is proposed so that the neuron and mechanical oscillator can be expressed in a uniform way. Thirdly, the relationship of parameters in the cyclic inhibitory CPG model with mechanical oscillators is illustrated, and the relationship expression of the control signal and sensor information for snake robots and the CPG states is given. Finally, the proposed method is verified with simulation, and the simulation results are analyzed. In this method, there are clear definitions for the control signals and sensor information of snake robots, and the computation complexity of CPG is decreased because neuron computation in CPG is replaced with physical structure of mechanical oscillators.

     

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